{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Titanic.ipynb",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "LY5QNoFFpJ0f",
        "outputId": "cabd2690-003b-4f0a-e31a-c38863d49328",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "# 导入基础库\n",
        "\n",
        "import numpy as np \n",
        "from numpy.random import RandomState\n",
        "import pandas as pd \n",
        "import matplotlib.pyplot as plt\n",
        "import tensorflow as tf \n",
        "from tensorflow.keras import models,layers\n",
        "\n",
        "tf.__version__"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'2.3.0'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 2
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YxkinG-tpK0i"
      },
      "source": [
        ""
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UgqGb4HUcde0",
        "outputId": "fd4c0158-1a7f-49d9-d583-d7fe1bf91ed0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        }
      },
      "source": [
        "# 下载测试数据\n",
        "# 数据源：https://www.openml.org/d/40945\n",
        "!wget https://www.openml.org/data/get_csv/16826755/phpMYEkMl -O ./titanic.csv"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "--2020-10-05 11:10:49--  https://www.openml.org/data/get_csv/16826755/phpMYEkMl\n",
            "Resolving www.openml.org (www.openml.org)... 131.155.11.11\n",
            "Connecting to www.openml.org (www.openml.org)|131.155.11.11|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: unspecified [text/plain]\n",
            "Saving to: ‘./titanic.csv’\n",
            "\n",
            "./titanic.csv           [  <=>               ] 114.98K   374KB/s    in 0.3s    \n",
            "\n",
            "2020-10-05 11:10:50 (374 KB/s) - ‘./titanic.csv’ saved [117743]\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wnsbvGSJoXbG",
        "outputId": "2cc5a09f-d081-41a1-eb67-13136bfdb36f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 391
        }
      },
      "source": [
        "# 查看测试数据\n",
        "df = pd.read_csv('./titanic.csv')\n",
        "\n",
        "df = df.replace({'?':np.nan})\n",
        "df = df.fillna(0)\n",
        "df['age'] = df['age'].astype(\"float32\")\n",
        "\n",
        "rng = RandomState()\n",
        "dftrain_raw = df.sample(frac=0.9, random_state=rng)\n",
        "dftest_raw = df.loc[~df.index.isin(dftrain_raw.index)]\n",
        "\n",
        "# Titanic 数据集的目标是根据乘客信息预测他们在 Titanic 号撞击冰山沉没后能否生存。\n",
        "print('dftrain_raw.shape', dftrain_raw.shape)\n",
        "print('dftest_raw.shape', dftest_raw.shape)\n",
        "\n",
        "# 结构化数据一般会使用 Pandas 中的 DataFrame 进行预处理。\n",
        "dftrain_raw.head(5)"
      ],
      "execution_count": 48,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "dftrain_raw.shape (1178, 14)\n",
            "dftest_raw.shape (131, 14)\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>pclass</th>\n",
              "      <th>survived</th>\n",
              "      <th>name</th>\n",
              "      <th>sex</th>\n",
              "      <th>age</th>\n",
              "      <th>sibsp</th>\n",
              "      <th>parch</th>\n",
              "      <th>ticket</th>\n",
              "      <th>fare</th>\n",
              "      <th>cabin</th>\n",
              "      <th>embarked</th>\n",
              "      <th>boat</th>\n",
              "      <th>body</th>\n",
              "      <th>home.dest</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>460</th>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>Jacobsohn, Mrs. Sidney Samuel (Amy Frances Chr...</td>\n",
              "      <td>female</td>\n",
              "      <td>24.0</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>243847</td>\n",
              "      <td>27</td>\n",
              "      <td>0</td>\n",
              "      <td>S</td>\n",
              "      <td>12</td>\n",
              "      <td>0</td>\n",
              "      <td>London</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>775</th>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>Doharr, Mr. Tannous</td>\n",
              "      <td>male</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2686</td>\n",
              "      <td>7.2292</td>\n",
              "      <td>0</td>\n",
              "      <td>C</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>239</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>Roebling, Mr. Washington Augustus II</td>\n",
              "      <td>male</td>\n",
              "      <td>31.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>PC 17590</td>\n",
              "      <td>50.4958</td>\n",
              "      <td>A24</td>\n",
              "      <td>S</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>Trenton, NJ</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>443</th>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>Hickman, Mr. Lewis</td>\n",
              "      <td>male</td>\n",
              "      <td>32.0</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>S.O.C. 14879</td>\n",
              "      <td>73.5</td>\n",
              "      <td>0</td>\n",
              "      <td>S</td>\n",
              "      <td>0</td>\n",
              "      <td>256</td>\n",
              "      <td>West Hampstead, London / Neepawa, MB</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>54</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>Carter, Master. William Thornton II</td>\n",
              "      <td>male</td>\n",
              "      <td>11.0</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>113760</td>\n",
              "      <td>120</td>\n",
              "      <td>B96 B98</td>\n",
              "      <td>S</td>\n",
              "      <td>4</td>\n",
              "      <td>0</td>\n",
              "      <td>Bryn Mawr, PA</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "     pclass  survived  ... body                             home.dest\n",
              "460       2         1  ...    0                                London\n",
              "775       3         0  ...    0                                     0\n",
              "239       1         0  ...    0                           Trenton, NJ\n",
              "443       2         0  ...  256  West Hampstead, London / Neepawa, MB\n",
              "54        1         1  ...    0                         Bryn Mawr, PA\n",
              "\n",
              "[5 rows x 14 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 48
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BJZxunkXdNLn"
      },
      "source": [
        "字段说明：\n",
        "\n",
        "- Survived:0代表死亡，1代表存活【y标签】\n",
        "- Pclass:乘客所持票类，有三种值(1,2,3) 【转换成onehot编码】\n",
        "- Name:乘客姓名 【舍去】\n",
        "- Sex:乘客性别 【转换成bool特征】\n",
        "- Age:乘客年龄(有缺失) 【数值特征，添加“年龄是否缺失”作为辅助特征】\n",
        "- SibSp:乘客兄弟姐妹/配偶的个数(整数值) 【数值特征】\n",
        "- Parch:乘客父母/孩子的个数(整数值)【数值特征】\n",
        "- Ticket:票号(字符串)【舍去】\n",
        "- Fare:乘客所持票的价格(浮点数，0-500不等) 【数值特征】\n",
        "- Cabin:乘客所在船舱(有缺失) 【添加“所在船舱是否缺失”作为辅助特征】\n",
        "- Embarked:乘客登船港口:S、C、Q(有缺失)【转换成onehot编码，四维度 S,C,Q,nan】\n",
        "\n",
        "利用Pandas的数据可视化功能我们可以简单地进行探索性数据分析 EDA（Exploratory Data Analysis）。\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "otMDUNIWdet3",
        "outputId": "cffbc7ed-cd5f-4541-df11-de607e18cc8f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 507
        }
      },
      "source": [
        "# label 分布情况\n",
        "%matplotlib inline\n",
        "%config InlineBackend.figure_format = 'png'\n",
        "ax = dftrain_raw['survived'].value_counts().plot(kind = 'bar',\n",
        "     figsize = (12,8),fontsize=15,rot = 0)\n",
        "ax.set_ylabel('counts',fontsize = 15)\n",
        "ax.set_xlabel('survived',fontsize = 15)\n",
        "plt.show()"
      ],
      "execution_count": 49,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 864x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6hv8Q6gLdzxd",
        "outputId": "f0daee0a-27cb-4406-cc76-ccf334f6cfda",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 507
        }
      },
      "source": [
        "# age 分布情况\n",
        "\n",
        "%matplotlib inline\n",
        "%config InlineBackend.figure_format = 'png'\n",
        "\n",
        "ax = dftrain_raw['age'].plot(kind = 'hist',bins = 20,color = 'purple',\n",
        "                    figsize = (12,8),fontsize=15)\n",
        "\n",
        "ax.set_ylabel('Frequency',fontsize = 15)\n",
        "ax.set_xlabel('Pclass',fontsize = 15)\n",
        "plt.show()"
      ],
      "execution_count": 90,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 864x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "f3l5h9OGe5hC",
        "outputId": "48e28744-f3ab-453a-f251-75dd42fc0bc2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 507
        }
      },
      "source": [
        "# age 和 label 的相关性\n",
        "\n",
        "%matplotlib inline\n",
        "%config InlineBackend.figure_format = 'png'\n",
        "ax = dftrain_raw.query('survived == 0')['age'].plot(kind = 'density',\n",
        "                      figsize = (12,8),fontsize=15)\n",
        "dftrain_raw.query('survived == 1')['age'].plot(kind = 'density',\n",
        "                      figsize = (12,8),fontsize=15)\n",
        "ax.legend(['survived==0','survived==1'],fontsize = 12)\n",
        "ax.set_ylabel('Density',fontsize = 15)\n",
        "ax.set_xlabel('Age',fontsize = 15)\n",
        "plt.show()\n"
      ],
      "execution_count": 91,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 864x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xOabOnm1fYvA",
        "outputId": "8b1350fa-9d05-47d3-ab80-4c9e2498651e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 258
        }
      },
      "source": [
        "# 数据预处理\n",
        "def preprocessing(dfdata):\n",
        "\n",
        "    dfresult= pd.DataFrame()\n",
        "\n",
        "    # Pclass\n",
        "    dfPclass = pd.get_dummies(dfdata['pclass']).astype(\"float32\")\n",
        "    dfPclass.columns = ['pclass_' + str(x) for x in dfPclass.columns ]\n",
        "    dfresult = pd.concat([dfresult,dfPclass], axis = 1)\n",
        "\n",
        "    # Sex\n",
        "    dfSex = pd.get_dummies(dfdata['sex']).astype(\"float32\")\n",
        "    dfresult = pd.concat([dfresult,dfSex], axis = 1)\n",
        "\n",
        "    # Age\n",
        "    dfresult['age'] = dfdata['age'].fillna(0)\n",
        "    # dfresult['age_null'] = pd.isna(dfdata['age']).astype('float32')\n",
        "\n",
        "    #SibSp,Parch,Fare\n",
        "    dfresult['sibsp'] = dfdata['sibsp'].astype('float32')\n",
        "    dfresult['parch'] = dfdata['parch'].astype('float32')\n",
        "    dfresult['fare'] = dfdata['fare'].astype('float32')\n",
        "\n",
        "    # Carbin\n",
        "    # dfresult['cabin_null'] =  pd.isna(dfdata['cabin']).astype('float32')\n",
        "\n",
        "    # Embarked\n",
        "    dfEmbarked = pd.get_dummies(dfdata['embarked'], dummy_na=True).astype(\"float32\")\n",
        "\n",
        "    dfEmbarked.columns = ['embarked_' + str(x) for x in dfEmbarked.columns]\n",
        "    if 'embarked_0' in dfEmbarked:\n",
        "      del dfEmbarked['embarked_0']\n",
        "      \n",
        "    dfresult = pd.concat([dfresult, dfEmbarked], axis = 1)  \n",
        "\n",
        "    return(dfresult)\n",
        "\n",
        "x_train = preprocessing(dftrain_raw)\n",
        "y_train = dftrain_raw['survived'].values\n",
        "\n",
        "x_test = preprocessing(dftest_raw)\n",
        "y_test = dftest_raw['survived'].values\n",
        "\n",
        "print(\"x_train.shape =\", x_train.shape )\n",
        "print(\"x_test.shape =\", x_test.shape )\n",
        "\n",
        "x_train.head(5)"
      ],
      "execution_count": 50,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "x_train.shape = (1178, 13)\n",
            "x_test.shape = (131, 13)\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>pclass_1</th>\n",
              "      <th>pclass_2</th>\n",
              "      <th>pclass_3</th>\n",
              "      <th>female</th>\n",
              "      <th>male</th>\n",
              "      <th>age</th>\n",
              "      <th>sibsp</th>\n",
              "      <th>parch</th>\n",
              "      <th>fare</th>\n",
              "      <th>embarked_C</th>\n",
              "      <th>embarked_Q</th>\n",
              "      <th>embarked_S</th>\n",
              "      <th>embarked_nan</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>460</th>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>24.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>27.0000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>775</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>7.2292</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>239</th>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>31.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>50.4958</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>443</th>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>32.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>73.5000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>54</th>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>11.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>120.0000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "     pclass_1  pclass_2  pclass_3  ...  embarked_Q  embarked_S  embarked_nan\n",
              "460       0.0       1.0       0.0  ...         0.0         1.0           0.0\n",
              "775       0.0       0.0       1.0  ...         0.0         0.0           0.0\n",
              "239       1.0       0.0       0.0  ...         0.0         1.0           0.0\n",
              "443       0.0       1.0       0.0  ...         0.0         1.0           0.0\n",
              "54        1.0       0.0       0.0  ...         0.0         1.0           0.0\n",
              "\n",
              "[5 rows x 13 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 50
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "B-PMfKrrlTNm",
        "outputId": "fe57c7e4-3ac0-4e69-bee7-a06544e6df82",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 224
        }
      },
      "source": [
        "x_test.head(5)"
      ],
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>pclass_1</th>\n",
              "      <th>pclass_2</th>\n",
              "      <th>pclass_3</th>\n",
              "      <th>female</th>\n",
              "      <th>male</th>\n",
              "      <th>age</th>\n",
              "      <th>sibsp</th>\n",
              "      <th>parch</th>\n",
              "      <th>fare</th>\n",
              "      <th>embarked_C</th>\n",
              "      <th>embarked_Q</th>\n",
              "      <th>embarked_S</th>\n",
              "      <th>embarked_nan</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>25.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>151.550003</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>50.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>247.520798</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>62</th>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>46.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>61.174999</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>79</th>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>55.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>25.700001</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>88</th>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>33.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>151.550003</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "    pclass_1  pclass_2  pclass_3  ...  embarked_Q  embarked_S  embarked_nan\n",
              "4        1.0       0.0       0.0  ...         0.0         1.0           0.0\n",
              "17       1.0       0.0       0.0  ...         0.0         0.0           0.0\n",
              "62       1.0       0.0       0.0  ...         0.0         1.0           0.0\n",
              "79       1.0       0.0       0.0  ...         0.0         1.0           0.0\n",
              "88       1.0       0.0       0.0  ...         0.0         1.0           0.0\n",
              "\n",
              "[5 rows x 13 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SicGPjhngBY_"
      },
      "source": [
        "# 定义模型\n",
        "\n",
        "使用Keras接口有以下3种方式构建模型：使用Sequential按层顺序构建模型，使用函数式API构建任意结构模型，继承Model基类构建自定义模型。此处选择使用最简单的Sequential，按层顺序模型。"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DoHAm5zQgGZQ",
        "outputId": "dca139a4-b19c-471a-a313-a945776094f7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 255
        }
      },
      "source": [
        "tf.keras.backend.clear_session()\n",
        "\n",
        "model = models.Sequential()\n",
        "model.add(layers.Dense(20,activation = 'relu',input_shape=(13,)))\n",
        "model.add(layers.Dense(10,activation = 'relu' ))\n",
        "model.add(layers.Dense(1,activation = 'sigmoid' ))\n",
        "\n",
        "model.summary()"
      ],
      "execution_count": 51,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "dense (Dense)                (None, 20)                280       \n",
            "_________________________________________________________________\n",
            "dense_1 (Dense)              (None, 10)                210       \n",
            "_________________________________________________________________\n",
            "dense_2 (Dense)              (None, 1)                 11        \n",
            "=================================================================\n",
            "Total params: 501\n",
            "Trainable params: 501\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6Rkl-Ms0gKv6"
      },
      "source": [
        "# 训练模型\n",
        "\n",
        "训练模型通常有3种方法，内置fit方法，内置train_on_batch方法，以及自定义训练循环。此处我们选择最常用也最简单的内置fit方法。\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eeHUu8_XgPEu",
        "outputId": "6ba04ec5-d6e1-4de0-cbda-e7ffbf741282",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "# 二分类问题选择二元交叉熵损失函数\n",
        "model.compile(optimizer='adam',\n",
        "            loss='binary_crossentropy',\n",
        "            metrics=['AUC'])\n",
        "\n",
        "history = model.fit(x_train,\n",
        "                    y_train,\n",
        "                    batch_size=64,\n",
        "                    epochs=30,\n",
        "                    validation_split=0.2 #分割一部分训练数据用于验证\n",
        "                   )\n"
      ],
      "execution_count": 52,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/30\n",
            "15/15 [==============================] - 0s 23ms/step - loss: 0.6459 - auc: 0.6449 - val_loss: 0.6001 - val_auc: 0.6717\n",
            "Epoch 2/30\n",
            "15/15 [==============================] - 0s 3ms/step - loss: 0.6245 - auc: 0.6622 - val_loss: 0.5822 - val_auc: 0.7223\n",
            "Epoch 3/30\n",
            "15/15 [==============================] - 0s 3ms/step - loss: 0.6153 - auc: 0.6776 - val_loss: 0.5708 - val_auc: 0.7496\n",
            "Epoch 4/30\n",
            "15/15 [==============================] - 0s 3ms/step - loss: 0.6031 - auc: 0.7022 - val_loss: 0.5595 - val_auc: 0.7800\n",
            "Epoch 5/30\n",
            "15/15 [==============================] - 0s 4ms/step - loss: 0.5905 - auc: 0.7228 - val_loss: 0.5453 - val_auc: 0.8034\n",
            "Epoch 6/30\n",
            "15/15 [==============================] - 0s 4ms/step - loss: 0.5779 - auc: 0.7530 - val_loss: 0.5313 - val_auc: 0.8040\n",
            "Epoch 7/30\n",
            "15/15 [==============================] - 0s 4ms/step - loss: 0.5517 - auc: 0.7776 - val_loss: 0.5147 - val_auc: 0.8168\n",
            "Epoch 8/30\n",
            "15/15 [==============================] - 0s 3ms/step - loss: 0.5431 - auc: 0.7799 - val_loss: 0.5224 - val_auc: 0.8177\n",
            "Epoch 9/30\n",
            "15/15 [==============================] - 0s 3ms/step - loss: 0.5422 - auc: 0.7892 - val_loss: 0.5074 - val_auc: 0.8218\n",
            "Epoch 10/30\n",
            "15/15 [==============================] - 0s 3ms/step - loss: 0.5266 - auc: 0.7997 - val_loss: 0.4995 - val_auc: 0.8259\n",
            "Epoch 11/30\n",
            "15/15 [==============================] - 0s 3ms/step - loss: 0.5192 - auc: 0.8045 - val_loss: 0.4958 - val_auc: 0.8301\n",
            "Epoch 12/30\n",
            "15/15 [==============================] - 0s 3ms/step - loss: 0.5156 - auc: 0.8141 - val_loss: 0.4907 - val_auc: 0.8297\n",
            "Epoch 13/30\n",
            "15/15 [==============================] - 0s 3ms/step - loss: 0.5117 - auc: 0.8106 - val_loss: 0.4903 - val_auc: 0.8308\n",
            "Epoch 14/30\n",
            "15/15 [==============================] - 0s 3ms/step - loss: 0.5117 - auc: 0.8088 - val_loss: 0.5007 - val_auc: 0.8288\n",
            "Epoch 15/30\n",
            "15/15 [==============================] - 0s 3ms/step - loss: 0.5003 - auc: 0.8180 - val_loss: 0.4873 - val_auc: 0.8365\n",
            "Epoch 16/30\n",
            "15/15 [==============================] - 0s 3ms/step - loss: 0.4988 - auc: 0.8216 - val_loss: 0.4869 - val_auc: 0.8384\n",
            "Epoch 17/30\n",
            "15/15 [==============================] - 0s 3ms/step - loss: 0.4986 - auc: 0.8203 - val_loss: 0.4910 - val_auc: 0.8381\n",
            "Epoch 18/30\n",
            "15/15 [==============================] - 0s 3ms/step - loss: 0.4947 - auc: 0.8220 - val_loss: 0.4859 - val_auc: 0.8426\n",
            "Epoch 19/30\n",
            "15/15 [==============================] - 0s 3ms/step - loss: 0.4915 - auc: 0.8247 - val_loss: 0.4890 - val_auc: 0.8349\n",
            "Epoch 20/30\n",
            "15/15 [==============================] - 0s 2ms/step - loss: 0.4890 - auc: 0.8243 - val_loss: 0.4788 - val_auc: 0.8402\n",
            "Epoch 21/30\n",
            "15/15 [==============================] - 0s 3ms/step - loss: 0.4802 - auc: 0.8304 - val_loss: 0.4717 - val_auc: 0.8386\n",
            "Epoch 22/30\n",
            "15/15 [==============================] - 0s 3ms/step - loss: 0.4776 - auc: 0.8353 - val_loss: 0.4770 - val_auc: 0.8439\n",
            "Epoch 23/30\n",
            "15/15 [==============================] - 0s 3ms/step - loss: 0.4893 - auc: 0.8255 - val_loss: 0.4713 - val_auc: 0.8412\n",
            "Epoch 24/30\n",
            "15/15 [==============================] - 0s 3ms/step - loss: 0.4875 - auc: 0.8241 - val_loss: 0.4976 - val_auc: 0.8320\n",
            "Epoch 25/30\n",
            "15/15 [==============================] - 0s 3ms/step - loss: 0.4786 - auc: 0.8315 - val_loss: 0.4745 - val_auc: 0.8403\n",
            "Epoch 26/30\n",
            "15/15 [==============================] - 0s 3ms/step - loss: 0.4704 - auc: 0.8388 - val_loss: 0.4719 - val_auc: 0.8425\n",
            "Epoch 27/30\n",
            "15/15 [==============================] - 0s 3ms/step - loss: 0.4744 - auc: 0.8357 - val_loss: 0.4670 - val_auc: 0.8429\n",
            "Epoch 28/30\n",
            "15/15 [==============================] - 0s 3ms/step - loss: 0.4706 - auc: 0.8348 - val_loss: 0.4693 - val_auc: 0.8415\n",
            "Epoch 29/30\n",
            "15/15 [==============================] - 0s 3ms/step - loss: 0.4673 - auc: 0.8368 - val_loss: 0.4939 - val_auc: 0.8294\n",
            "Epoch 30/30\n",
            "15/15 [==============================] - 0s 2ms/step - loss: 0.4800 - auc: 0.8281 - val_loss: 0.4649 - val_auc: 0.8415\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_knqsu0G4LfS"
      },
      "source": [
        "# 评估模型\n",
        "\n",
        "我们首先评估一下模型在训练集和验证集上的效果。\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HwrkkORv4Jsy"
      },
      "source": [
        "%matplotlib inline\n",
        "%config InlineBackend.figure_format = 'svg'\n",
        "\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "def plot_metric(history, metric):\n",
        "    train_metrics = history.history[metric]\n",
        "    val_metrics = history.history['val_'+metric]\n",
        "    epochs = range(1, len(train_metrics) + 1)\n",
        "    plt.plot(epochs, train_metrics, 'bo--')\n",
        "    plt.plot(epochs, val_metrics, 'ro-')\n",
        "    plt.title('Training and validation '+ metric)\n",
        "    plt.xlabel(\"Epochs\")\n",
        "    plt.ylabel(metric)\n",
        "    plt.legend([\"train_\"+metric, 'val_'+metric])\n",
        "    plt.show()\n"
      ],
      "execution_count": 53,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DIEHziW74R_O",
        "outputId": "5bf17e77-894b-4366-8c00-957b81e34412",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 391
        }
      },
      "source": [
        "plot_metric(history,\"loss\")"
      ],
      "execution_count": 54,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
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    },
    {
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      "metadata": {
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        "colab": {
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          "height": 391
        }
      },
      "source": [
        "plot_metric(history, \"auc\")"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DeRACVJi4ldJ",
        "outputId": "2a1422b2-c879-4433-9800-a4d9980209ba",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        }
      },
      "source": [
        "model.evaluate(x = x_test,y = y_test)"
      ],
      "execution_count": 55,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "5/5 [==============================] - 0s 2ms/step - loss: 0.5342 - auc: 0.8077\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[0.5342241525650024, 0.8077467679977417]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 55
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hdCyWFT4jR_B"
      },
      "source": [
        "# 使用模型"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-MZol1IAjRiX",
        "outputId": "b0d4d828-0933-4c5e-b1e6-0d0e98e9639c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 187
        }
      },
      "source": [
        "# 预测概率\n",
        "model.predict(x_test[0:10])\n",
        "#model(tf.constant(x_test[0:10].values,dtype = tf.float32)) #等价写法"
      ],
      "execution_count": 57,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[0.8596355 ],\n",
              "       [0.19167662],\n",
              "       [0.95181733],\n",
              "       [0.8875871 ],\n",
              "       [0.88463414],\n",
              "       [0.23663831],\n",
              "       [0.84617376],\n",
              "       [0.46467987],\n",
              "       [0.96962833],\n",
              "       [0.7131544 ]], dtype=float32)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 57
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CR8RKo5YjZG7",
        "outputId": "5c8a5732-9f1f-46eb-8011-7bd1aab12f1a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 258
        }
      },
      "source": [
        "# 预测类别\n",
        "model.predict_classes(x_test[0:10])"
      ],
      "execution_count": 58,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:From <ipython-input-58-1e375f0a0a40>:2: Sequential.predict_classes (from tensorflow.python.keras.engine.sequential) is deprecated and will be removed after 2021-01-01.\n",
            "Instructions for updating:\n",
            "Please use instead:* `np.argmax(model.predict(x), axis=-1)`,   if your model does multi-class classification   (e.g. if it uses a `softmax` last-layer activation).* `(model.predict(x) > 0.5).astype(\"int32\")`,   if your model does binary classification   (e.g. if it uses a `sigmoid` last-layer activation).\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[1],\n",
              "       [0],\n",
              "       [1],\n",
              "       [1],\n",
              "       [1],\n",
              "       [0],\n",
              "       [1],\n",
              "       [0],\n",
              "       [1],\n",
              "       [1]], dtype=int32)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 58
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3eAC2F-1jeus"
      },
      "source": [
        "# 保存模型\n",
        "\n",
        "可以使用Keras方式保存模型，也可以使用TensorFlow原生方式保存。前者仅仅适合使用Python环境恢复模型，后者则可以跨平台进行模型部署。推荐使用后一种方式进行保存。\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZrIMoohWjkIF"
      },
      "source": [
        "# 保存模型结构及权重\n",
        "\n",
        "model.save('./data/keras_model.h5')  \n",
        "\n",
        "del model  #删除现有模型\n",
        "\n",
        "# identical to the previous one\n",
        "model = models.load_model('./data/keras_model.h5')\n",
        "model.evaluate(x_test,y_test)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QM8e1KDhjkqB"
      },
      "source": [
        "# 保存模型结构\n",
        "json_str = model.to_json()\n",
        "\n",
        "# 恢复模型结构\n",
        "model_json = models.model_from_json(json_str)\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zFrVi1y8jmS3"
      },
      "source": [
        "#保存模型权重\n",
        "model.save_weights('./data/keras_model_weight.h5')\n",
        "\n",
        "# 恢复模型结构\n",
        "model_json = models.model_from_json(json_str)\n",
        "model_json.compile(\n",
        "        optimizer='adam',\n",
        "        loss='binary_crossentropy',\n",
        "        metrics=['AUC']\n",
        "    )\n",
        "\n",
        "# 加载权重\n",
        "model_json.load_weights('./data/keras_model_weight.h5')\n",
        "model_json.evaluate(x_test,y_test)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PDrGeHzkjpEz"
      },
      "source": [
        "# TensorFlow 原生方式保存"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0H02LZV7jrW3"
      },
      "source": [
        "# 保存权重，该方式仅仅保存权重张量\n",
        "model.save_weights('./data/tf_model_weights.ckpt',save_format = \"tf\")\n",
        "# 保存模型结构与模型参数到文件,该方式保存的模型具有跨平台性便于部署\n",
        "\n",
        "model.save('./data/tf_model_savedmodel', save_format=\"tf\")\n",
        "print('export saved model.')\n",
        "\n",
        "model_loaded = tf.keras.models.load_model('./data/tf_model_savedmodel')\n",
        "model_loaded.evaluate(x_test,y_test)"
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}